If either match attempt succeeds, the algorithm attempts to match entities in the same fashion and the "now point" of the rewritten story is incremented.

the algorithm does not looks back in the rewritten story for a match.


Spoken dialog corpora allow for a multidimensional analysis of speech recognition and language understanding models of dialog systems.

language models can be directly trained based either on the dialog history or its equivalence class (or cluster).


One possible explanation is the limited length of the dialogs in the HMIHY corpus (4 turns on average).

the dialog context and history are negligible compared to the system prompt alone.


The calltype labels are relevant as we are interested here in clusters that can be semantically different.

a feature vector representing an utterance is a first order statistic on the calltypes.


One approach for solving this problem is via representing it as an SVM (Vapnik, 1998)  , such that the score of ) is higher than the scores of all other parses for the sentence.

we optimize for: are slack variables used to handle the non-separable case.


Additionally, for reducing sparsity, for each keyed string , we also include a keyed string .

each projection path occurs twice in the list representation of the tree -once headed by the word, and once by its le-type.


In the following examples, the node labels are abbreviated as well; is a special symbol for end of path and e is a special symbol for start of path.

the features that contain the node will be:  The features of this model are defined by the two templates: .


In terms of 1 -norm regularization, feature sparsity can be explained by the following observation: Since every non-zero parameter weight incurs a regularizer penalty of γ|λ i |, its contribution to minimizing the negative loglikelihood has to outweigh this penalty.

parameters values where the gradient at λ = 0 is can be kept zero without changing the optimality of the solution.


In the end, the performance of the parser with respect to its evaluation criteria-typically accuracy, and perhaps also speed-are all that matter.

the engineer must understand what the model is doing only to the point that it helps make the model perform better.


We also came to the conclusion that they are not the kind of words we want to have in the cache, since their presence in a sentence implies little about their presence in the next.

the resulting model is of the form: every f cache sw has a corresponding weight γ sw for the calculation of the probability of w.   We implemented two versions of the model, one in which we estimated only one cache feature weight for the whole model and another in which we estimated one cache feature weight for every word pair in the model.


Usually content words in both source and target languages will be emphasized by large term weights.

good phrase translation pairs will share similar contours, or, to express it in a different way, will be close to each other in the term weight vector space.


The following algorithm summarizes the decoding process when not considering word reordering: Current node n, previous node n'; edge e Language model state L, L' Hypothesis h, h' Foreach node n in the lattice Foreach incoming edge e in n phrase = word sequence at e n' = FromNode(e) foreach L in n' foreach h with LMstate L LMcost = 0.0 foreach word w in phrase The updated hypothesis h' at the current node stores the pointer to the previous hypothesis and the edge (labeled with the target phrase) over which it was expanded.

at the final step, one can trace back to get the path associated with the minimum cost, i.e.


The co-occurring words should share similar term frequency and document frequency statistics.

the vectors s have similar term weight contours corresponding to the co-occurring word pairs.


Again, the values of 0.5 and 1.5 are constants used in IR tasks acting as smoothing.

after a transducer is extracted from a parallel corpus, tf and df are counted from the collection of the "bag-of-words'' phrase alignment representations.


Equation 1provides a evidence of the translation quality based on the word alignment probability, and can be assumed to be different from the evidences in vector space.

a natural way of integrating them together is a geometric interpolation shown in (10) or equivalently a linear interpolation in the log domain.


0 = β gave the best performance in most cases.

equation (10) can be simplified into: The phrase translation score functions in (1) and (11) are non-symmetric.


Because each phrase pair is treated as a "bag-of-words", the grammar structure is not well represented in the vector model.

our model is more tuned towards the adequacy aspect, corresponding to NIST score improvement.


For example, even alignments with no change in word order may not be possible if the structures of the two trees are radically mismatched.

it is helpful to allow departures from the constraints of the parallel bracketing, if it can be done in without dramatically increasing computational complexity.


A new parameter P swap gives the probability of the upper node in the elementary tree in English corresponding to the lower node in Chinese, and vice versa.

the probability for the following transformation: is factored as P elem (AB|A⇒B) P swap P t (A |A) P t (B |B) P align ({(1, 1)(2, 2)}|A ⇒ XY ).


, x T as: In our formulation, CRFs deal with word boundary ambiguity.

the the size of output sequence T is not fixed through all candidates y ∈ Y(x).


By length bias, long tokens are preferred to short tokens.

larger number of l-errors implies that the result is highly influenced by the length bias.


Our results only showed a small (if significant) improvement in precision (19.86%), that we didn't feel would justify the considerable effort required to develop a proper name detector.

we looked for simpler but more effective ways to improve the algorithm.


So far, we have a list of instances, which are labeled as BP, for each BP pattern, however, many of the cases are not BPs.

we need to revise automatically the classification of each case using the dictionary-based approach (discussed in section 3.3).


In the formulation of the maximum entropy tagging model, we assume that the probability distribution of tags takes the form of an exponential distribution, parameterized by a sequence of feature weights, λ m 1 , where there are m-many features.

we obtain a distribution for P r λ m 1 (t i t i−1 ,w) of the form: where Z t i−1 ,w is a normalizing factor.


Furthermore, based on t and d, we can calculate the depth (d − ∆t, see Equation 5) we must have been at previously.

we must take the value of A p−1,d−∆t,t which is the probability of having arrived at position p − 1 at depth d − ∆t with tag t .


In tallying frequencies we treated all numeric expressions as occurrences of a special term, "*num*".

the tokens "1989" and "10,000" are treated as instances of the same term, and clustering has no opportunity to distinguish, say, years from monetary amounts.


As noted above, TIME occurs only very infrequently.

little training data is available for this field and mistakes (BWI missed one of the three instances in the test set) have a large effect on the TIME-specific scores.


Unfortunately, this is not the case in practice.

the usual approach is to approximate the true but unknown distribution by a model distribution ).


Thus we obtain a probability model whose structure fits into the mathematical framework of so-called Hidden Markov Model (HMM).

this approach is often also referred to as HMM-based POS tagging.


To apply the Bayes decision rule for minimum symbol error rate, we first compute the marginal Again, since the model is a second-order model, the sum over all possible POS tag strings g N 1 (with g m = g) can be computed efficiently using a suitable extension of the forward-backward algorithm (Bahl et al., 1974).

we obtain the decision rule for minimum symbol error at positions m = 1, ..., N : Here, after the the marginal probability p m (g, w N 1 ) has been computed, the task of finding the most probable POS tag at position m is computationally easy.


For the minimum symbol error, the marginal (and posterior) probability p m (g|w N 1 ) has to be computed: which, due to the specific structure of the model p(g n |g n−1 n−2 , w n+2 n−2 ), can be calculated efficiently using only a forward algorithm (without a 'backward' part).

we obtain the decision rule for minimum symbol error at positions m = 1, ..., N : As in the case of the generative model, the computational effort is to compute the posterior probability p m (g|w N 1 ) rather than to find the most probable tag at position m. So far, we have said nothing about how we train the free parameters of the model distributions.


Rightmost extension defines a canonical search space in which one can enumerate all subtrees from a given set of trees.

we here consider an upper bound of the gain that allows subspace pruning in  (Morhishita, 2002), gives a convenient way of computing a tight upper bound on gain( t , y ) for any super-tree t of t. For any t ⊇ t and y ∈ {±1}, the gain of t , y is bounded by µ(t) (i.e., gain( t y ) ≤ µ(t)), where µ(t) is given by If we focus on the case y = +1, then for any t ⊇ t and y ∈ {±1}, we can efficiently prune the search space spanned by right most extension using the upper bound of gain u(t).


First, we believe that it is more impor-tant for the ontology to reflect observed linguistic behavior than to reflect any abstract view of the space of possible speech acts.

the taxonomy of verbs contains concepts that are atomic linguistically, but combine several illocutionary points.


In this case, we simply use  Our DT growing algorithm in Section 3.1.1 is still based on a greedy approach.

it is not guaranteed to construct the optimal DT.


The randomized history clustering in the RF-trigram makes it possible to compute probabilities of most test data events without relying on backoff.

the RF-trigram can effectively increase the probability of those events that will otherwise be backoff to lower order statistics.


The RF approach results in a random history clustering which greatly reduces the number of unseen events compared to the KN smoothing, even though the same training data statistics are used.

this new approach can generalize well on unseen test data.


For example, for a joint occurrence of two singletons in 500,000 samples, G 2 is approximately 28.24.

if we wanted to use G 2 as our measure of association, we could compare the number of word pairs expected by chance to have a G 2 score greater than or equal to 28.24 with the number of word pairs observed to have a G 2 score greater than or equal to 28.24, and compute expected noise and precision just as we did with p-values.


A priori, this is at least as plausible a measure of strength of association as is the degree to which we should be surprised by the joint frequency of the two words.

even if G 2 turns out to be bad for estimating significance, it does not follow that it is therefore a bad measure of strength of association.


In this respect, (Xing et al., 2002) shares the same objective as ours.

we require an optimization over all the clusters in the training data.


By way of this correspondence, Japanese sentences are divided into 10,610 clusters.

each cluster consists of Japanese sentences that are possible translations from the same English seed sentence that the cluster has.


From the equation 3, this means a Euclidean distance of M 1/2 X k = M 1/2 V k X.

v k may subsume the effect of M in a preprocessing stage.


Here we prove theorem 1, namely deriving M that satisfies the condition under the constraint Expanding (8), we get and from (9), for all k where M kl denotes an adjugate matrix of m kl .

we come to minimize (10) under the constraint (11).


Evidently, simple combination of the results of the three classifiers leaves much room for improvement.

we explore other ways for combining these results.


Besides that, some English NEs are translated via transliteration (Lee and Chang, 2003;Al-Onaizan and Knight, 2002;Knight and Graehl, 1997) instead of semantic translation.

it is fairly important to make transliteration models.


The bigger the difference is, the less probable they can be translations of each other.

we define the distortion score between the source English NE and the candidate Chinese NE as another feature.


The HMM model (Vogel et al., 1996) conducts word alignment with a strong tendency to preserve localization from one language to another.

we extract NE alignments based on the results of these two models as our baseline systems.


The correct identification of the domain of the text is a crucial point for Domain Driven Disambiguation, an unsupervised Word Sense Disambiguation (WSD) methodology that makes use of only domain information.

dRE has been exploited and evaluated in the context of a WSd task.


For this reason, a FACTOTUM label has been created that basically includes generic synsets, which appear frequently in different contexts.

the FACTOTUM domain can be thought of as a "placeholder" for all other domains.


For instance, the original language, or the topic and style (and hence vocabulary) usually stays the same over many sentences in sequence.

when we collect only 300 sentences in sequence, certain better test sets of 300 sentences may be constructed by sampling these sentences from different parts of the corpus.


It may be therefore useful to indicate and incorporate into the model the "strength" of the connection between two vertices § a nd § ¡ as a weight ¢ added to the corresponding edge that connects the two vertices.

we introduce a new formula for graph-based ranking that takes into account edge weights when computing the score associated with a vertex in the graph.


In our factoid representation, a distinction between "killed" and "feared dead" would be made, and different numbers of people mentioned would have been differentiated.

the factoid approach can capture much finer shades of meaning differentiation than DUC-style information overlap does.


We summarise our results as follows: Factoids can be defined with high agreement by independently operating annotators in naturally occurring text (K=.70) and independently annotated with even higher agreement (K=.86 and .87).

we consider the definition of factoids intuitive and reproducible.


However, pushing causes an imbalance of weights among paths if the WFST is not deterministic.

of this imbalance, we cannot compare path costs when pruning.


To reduce the ambiguity of the composed WFST, the states are merged according to the statistics of hypotheses while decoding.

we reduced decoding time to approximately of dynamic composition of submodels, which corresponds to the conventional approach.


The tectogrammatical level of the PDT was motivated by the ever increasing need for large corpora to include not only morphological and syntactic information but also semantic and discourse-related phenomena.

the tectogrammatical trees have been enriched with features indicating the information structure of sentences which is a means of showing their contextual potential.


This therefore includes only mentions identified as being in the ACE semantic categories by both the key and the system response.

these scores cannot be directly compared against coreference scores involving all noun phrases.


Since we can compute the entity self-similarity ., using the identity map), we are now ready to define the precision, recall and F-measure as follows: A @ reference and system entities, and entities not aligned do not get credit.

the F-measure (5) penalizes a coreference system that proposes too many (i.e., lower precision) or too few entities (i.e., lower recall), which is a desired property.


The assumption is that in a document set to be summarized (which consists of related news reports), references to people with the same affiliation and role are likely to be references to the same person, even if the names do not match due to spelling errors.

we form one AVM for each person, by combining AVMs.


Prune the AVM of values that occur with a frequency below an empirically determined threshold.

we obtain the following AVMs for the three examples above: This is the input semantics for our generation module described in the next section.


After clustering, all sentences within a cluster should represent similar information.

similar noun phrases in sentences within a cluster are likely to refer to the same entities.


The natural way to determine how dependent the distributions of ® andā re is to calculate their mutual information (Church and Hanks, 1991): If the occurrences of ® and¯were completely independent of each other, we would expect the maximum likelihood probability .

mutual information is zero when ® and¯are independent, and positive otherwise.


The average scores before and after rewrite were % and x g respectively for fluency and % g and t Õ respectively for accuracy.

the rewrite operations increases both scores by around 0.2.


Successfully detecting SR errors can speed up the entire process of error correction.

we focus on error detection in this study.


Although statistical learning methods have been widely used in confidence annotation (Carpenter et al., 2001;Pao et al., 1998;Chase, 1997), their results are difficult to interpret.

we select TBL to derive error patterns from the SR output in this study.


Furthermore, lexicalized parsers are desired to support error detection at the word level.

we select Link Grammar 1 to generate syntactic features.


Despite its best performance in linguistic feature groups, L-Local-Long produces worse performance than CS in both CER and F measure.

linguistic features by themselves are not as useful as confidence scores.


This might be too aggressive in the case of the All variant, especially for longer or multi-topic audio documents.

we investigated other aggregation schemes that only required words to cohere well with a subset of the words in N(w).


The third aggregation scheme was to set SC(w i ) to the average of the 3 most similar neighbors in N(w) (the 3MAX variant).

there are altogether 2x2x3 = 12 possible configurations of the algorithm.


We treat each of these statements as a separate incident; the containment relations among them are beyond the scope of our current goals.

5 each incident corresponds to a partial description of an outbreak, over a period of time and geographic area.


Finally, given the distribution of the data, it is difficult to find the right features to distinguish between instances.

it is prudent to use as many features as possible and let the learning algorithm filter out the redundant features.


The possible alignment having the best overall score is selected as the word alignment for that sentence pair.

for a sentence pair (e, f ) we seek the alignmentâ such that where the f i are features and the λ i are weights.


The assessors' judgments for the topics were remarkable in that only a median of 2% of the sentences were judged to be relevant, despite the documents themselves being relevant.

nearly every relevant sentence (median of 93%) was declared novel.


It is easy to see that topics 98, 83, 82, and 67 exhibit a wide range of performance, but all have an F score of close to 0.6.

two runs with equal F scores may be performing quite differently, and a difference in F scores can be due to changes in precision, recall, or both.


From the study of the distributions of scores assigned to stories by the baseline and SVM model systems, we can see that we now do a better job of detecting old stories (reducing false alarms).

we believe that attacking the problem as "old story detection" might be a better and more fruitful approach.


instances that share coherent features) are represented by similar vectors in the reduced dimensionality space.

a document would be considered similar to a category ID if the seed terms and the document terms tend to co-occur overall in the given corpus.


It is pointed that extremely unbalanced kernel values cause overfitting.

collins and Duffy (2001) used W (S i ) = λ (# of productions in S i ) , and Kashima and Koyanagi (2002)  is a factor to alleviate the unbalance by penalizing large subtrees.


However, it can be applied to only a few types of subtrees that can be represented as a contiguous part in a string representation of a tree.

neither K r lo nor K r c can be sped up by using this procedure.


The cost of FREQTM is much smaller than that of SVM training with DP.

our method is beneficial even if we train the SVM only once.


Although FREQTM also has a superlinear cost, it is smaller than that of SVM training.

the cost of SVM training will become a problem before the cost of FREQTM does.


However, N-gram based methods have a critical problem; they cannot consider co-occurrences with gaps, although the LCS-based method can deal with them.

lin and Och (2004) introduced skip-bigram statistics for the evaluation of machine translation.


From the table, there are fifteen subsequences 5 that are common to S1 and S2.

for reference, there are three unigrams, one bigram, zero trigrams and three skipbigrams common to S1 and S2.


Since summary should not miss important information given in the human reference, recall is more important than precision.


Before we use the one human written reference summary as the gold-standard-reference, to examine variations among human assessors, we prepared two additional human summaries for each topic sets.

we obtained three reference summaries and evaluation results for each topic sets (Table 2).


The results show that every pair has a high correlation.

changing the human subject has little influence as regards creating references and evaluating system summaries.


The bin determines the weight of the contribution of each occurrence of the query term in the ranking score.

the relevance score is just the weighted sum of the numbers of occurrences within each bin.


This number was linearly decreasing with the position of the relevant document in the pre-order.

the higher the document was positioned in the pre-order, the more times it was selected for pairing (training).


However, it also gives the upper bound of performance of a discretized gw.lw combination assuming that the loss due to discretization is negligible which can be easily attained by using sufficiently large number of bins.

the results indicate that gw.lw, which includes practically all the popular "bag of words" ranking formulas such as tf.idf, BM25 or language models, has almost reached its upper limit and other classes of representations and ranking formulas need to be explored to attempt greater improvements.


In order to test whether our approach can exceed the baseline performance we set BM25 to be our starting ranking function (SRF).

in this case:  Table 3 shows performance for the 8 by 8 bin design.


However allowing reordering in translation is computationally expensive and in some cases even provably NP-complete (Knight, 1999).

any translation scheme that incorporates reordering must necessarily balance model complexity against the ability to realize the model without approximation.


Previous approaches to WFST-based reordering (Knight and Al-Onaizan, 1998;Kumar and Byrne, 2003;Tsukada and Nagata, 2004) constructed permutation acceptors whose state spaces grow exponentially with the length of the sentence to be translated.

these acceptors have to be pruned heavily for use in translation.


While the model uses a single block to split the sentence pair into two independent regions, it is not clear which block we should select for this purpose.

we treat the splitting block as a hidden variable.


a is the alignment vector with a j mapping the position of the English word e a j to which f j connects.

we have the standard limitation that one foreign word cannot be connected to more than one English word.


δ e segments e into two parts: (δ e , e) = (δ e ∈ , δ e / ∈ ).

the inner part δ e ∈ = {e the block δ [] splits the parallel sentence pair into two non-overlapping regions: the Inner δ ∈ parts (see Figure 1).


Contrast this with Viterbi alignment where one computeŝ We observe, in parallel corpora, that when one word translates into multiple words in another language, it usually translates into a contiguous sequence of words.

we impose a contiguity constraint on word alignments.


However, if the overall system is evaluated using a metric other than precision (e.g., recall), the accuracy mechanism may not guarantee that the best rule is chosen at each step.

we choose the best rule according to the evaluation metric to be used for the overall system.


In each step, Viterbi stores and updates |vocabulary size| N many δ values-unfeasibly many except for very small N .

in Table  1 we develop a Viterbi beam search algorithm which is linear in T and in the beam width.


For instance, although two features may carry rich discriminative information when treated separately, there may be very little gain if they are combined in a feature vector, because of the high correlation between them.

in what follows, we describe a technique of incorporating correlation information in the feature selection criterion.


As Vapnik (1999) pointed out, the most robust linear models are the ones that achieve the least training errors with the least number of features.

the robustness of the MSR algorithm are mainly affected by the feature selection method.


One problem is that some critical but low probability information from individual modalities (e.g., recognized alternatives with low probabilities) may never reach the fusion stage.

this paper addresses how to use information from one modality (e.g., deictic gesture) to directly influence the semantic processing of another modality (e.g., spoken language understanding) even before the fusion stage.


For example, given an interface, a point or a circle on the screen could result in selection of different entities with different probabilities.

in Equation 1, is the selection probability which indicates the likelihood of selecting an entity e given a gesture at time t i .


Furthermore, given an entity , and are not dependent on time t i , but rather on the domain and the use of language expressions.

they can be estimated based on the training data that are annotated in terms of semantic information and the intended objects of interest (as discussed in Section 4.1).


Since the annotated data is very limited, the sparse data can become a problem for the maximum likelihood estimation.

a smoothing technique based on the Katz backoff model (Katz, 1987) is applied.


Even though the first two systems operate in similar domains (information access), they have very different user populations: students and faculty on campus in the first case versus the entire Pittsburgh community in the second case.

the two systems were configured with different error handling strategies and policies (see Table 1).


Unfortunately, the original transcription effort did not provide punctuation guidelines, and the Switchboard treebanking was performed on the transcript unchanged, without reference to the audio.

the sentence boundaries sometimes do not match human listener decisions using SU annotation guidelines, with differences mainly corresponding to treatment of discourse markers and backchannels.


While it had been claimed that PCFGs perform poorly as edit detectors (Charniak and Johnson, 2001), we could not find empirical evidence in the literature quantifying the severity of the problem.

we evaluated two PCFGs (Bikel, 2004;Charniak and Johnson, 2005) on edit detection and compared their performance to a state-of-the-art TAG-based edit detection system .


Each panel has the horizontal axis representing location or order of sentence in a document, and the vertical axis the proportion of the times sentences at particular locations are picked as relevant to summarization.

in G1K3, we see that the first sentence (to appear in a document) gets voted for about 12% of the time, while the 26th sentence is voted for less than 2% of the time.


1 Since the business of extractive summarization, such as one we are concerned with here, is about ranking sentences according to how useful/important they are as part of summary, we will consider here a particular ranking scheme based on the probability of a sentence being part of summary under a given DOV, i.e., where y denotes a given sentence, and stands for a DOV, an array of observed vote counts for sentences in the text; v 1 refers to the count of votes for a sentence at the text initial position, v 2 to that for a sentence occurring at the second place, etc.

given a four sentence long text, if we have three people in favor of a lead sentence, two in favor  Now suppose that each sentence y i (i.e., a sentence at the i-th place in the order of appearance) is associated with what we might call a prior preference factor θ i , representing how much a sentence at a particular position is favored as part of a summary in general.


Attributes for C4.5 are broadly intended to represent some aspects of a sentence in a document, an object of interest here.

for each sentence ψ, its encoding involves reference to the following set of attributes or features.


Users are typically more intolerant to seeing errors on the strings that used to be converted correctly than seeing errors that also existed in the previous version.

it is crucial that when we adapt to a new domain, we do so by introducing the smallest number of side effects, particularly in the case of an incremental adaptation to the domain of a particular user, i.e., to building a model with incremental learning capabilities.


Compared to our datasets, Ratnaparkhi's dataset (Ratnaparkhi et al., 1994) contains only the lexical heads v, n 1 , p and n 2 .

our methodology cannot be applied to Ratnaparkhi's dataset (RRR).


This feature is a modified form of the sub-categorization feature described in (Pradhan et al., 2003): the differences in various part-of-speeches for the verb were ignored and the preposition that heads a prepositional phrase is also attached to the label.

for the sentence "The stock declined in June by 4%", the value for this feature is *-PPin-PPby.


The criterion for selecting the best set of features was the accuracy on the cross-validation.

the development of the models was performed entirely on the training set, which acted also as a development set.


In all these schemes (even A*), processing promising items as soon as possible risks having to reprocess them if their values change later.

this strategy should be balanced against the "topological sort" strategy of waiting to process an item until its value has (probably) converged.


16 It is correct for proofs that incorporate two copies of a's value, or-more important-no copies of a's value because a is a side condition.

it recognizes that a low-probability item must have high priority if it could be used as a side condition in a higher-probability parse (though this cannot happen for the side conditions derived by the magic templates transformation ( §6)).


However, it is hard to manually construct such patterns since it largely depends on the knowledge of the pattern designer.

we prefer patterns that can be automatically derived from training data.


As we have seen with heuristic informers, polluting the informer pool can significantly hurt SVM accuracy.

we also use the 3-state "begin/in/out" (BIO) model.


Each x i leads to a column vector of observations.

we use matrix notation to write down x: A table cell is addressed as x[i, ] where i is the token position (column index) and is the level or row index, 1-6 in this example.


In general, the attributes of an entry can provide useful cues for predicting whether it should be selected.

we can perform content selection by applying a standard classifier on each entry.


Our goal is to select a subset of database entities that maximally satisfies linking constraints and is as consistent as possible with the individual preferences of each entity.

content selection can be naturally stated as an optimization problem -we wish to find a label assignment that minimizes the cost of violating the above constraints.


Thus, content selection can be naturally stated as an optimization problem -we wish to find a label assignment that minimizes the cost of violating the above constraints.

let C + and C − be a set of selected and omitted entities, respectively; ind + (x) and ind − (x) are scores that capture the individual preference of x to be either selected or omitted, and link l (x, y) reflects the degree of dependence between the labels of x and y based on a link of type l. the optimal label assignment for database entries x 1 , .


The type and number of the attributes are determined by the entity type.

we separately estimate individual preference scores for each entity type.


An obvious manifestation of semantic relatedness is attribute sharing.

we consider links across entities with one or more shared attributes.


In addition, three pairs produced by the program did not match the goldstandard alignment.

the automatic method achieved 94.0% precision and 90.4% recall.


For example, the reader may feel strongly about the quality of the gym in a hotel, whereas many reviewers may focus on other aspects of the hotel, such as the decor or the location.

the reader is forced to wade through a large number of reviews looking for information about particular features of interest.


For many words, a word sense or set of senses is used throughout the review corpus with a consistently positive, negative or neutral connotation (e.g., "great", "awful", etc.).

in many cases, a word w's SO label in the context of a feature f and sentence s will be the same as its SO label in the context of other features and sentences.


Nasukawa, Yi, and colleagues  classify the contextual polarity of sentiment expressions, as we do.

their work is probably most closely related to ours.


In many applications, including Geographic Information Systems (GIS), it is necessary to interpret a given toponym mention as a particular entity in a geographical database or gazetteer.

the mention "Washington" in "He visited Washington last year" will need to be interpreted as a reference to either the city Washington, DC or the U.S. state of Washington, and "Berlin" in "Berlin is cold in the winter" could mean Berlin, New Hampshire or Berlin, Germany, among other possibilities.


Of the 13,860 toponyms that were ambiguous according to GNIS, 1827 of them were found in MAC1, of which only 588 had discriminators within a ±5-word window (i.e., discriminators which matched gazetteer features that disambiguated the toponym).

67.82% of the 1827 toponyms found in MAC1 that were ambiguous in GNIS lacked a discriminator.


The second approach tries to solve the semantic combinations we come upon in text corpora.

we analyzed the RTE development corpus and we devised rules for some of the R i •R j combinations that we encountered.


Notice that the cost of each matching, M , implicitly depends on the current setting of the weights w. It can be shown that since each c(H, T ; w) involves minimizing M ∈ M, which depends on w, it is not convex.

we can't hope to globally optimize our cost functions over w and must settle for an approximation.


• True word senses usually correlate with document or passage topic.

choose a measure of similarity between documents (e.g., the cosine measure in TF/IDF space).


The approach taken was to deliberately select constituent words that could be linked to a sense of the original query word through WordNet.

the additional ambiguity added to the collection models any underlying relatedness expressed by the original senses of the query word.


There is supporting evidence from Sanderson and Van Rijsbergen (1999) to suggest that accounting for this frequency bias is in some way advantageous.

it is worth considering that simulating a uniform accuracy and error rate across all words and senses might actually offer a pessimistic picture of the potential for disambiguation and IR.


However, the size of modern IR test collections dictates that future studies will need to rely more heavily on simulation.

until such time that a significant manually disambiguated IR collection exists pseudowords remain an interesting way to explore the effects of ambiguity within a large collection.


( ) Different types of NEs have different structures and intrinsic characteristics.

a single model can't capture all types of entities.


Existing named entity (NE) transliteration approaches often exploit a general model to transliterate NEs, regardless of their origins.

both a Chinese name and a French name (assuming it is already translated into Chinese) will be translated into English using the same model, which often leads to unsatisfactory performance.


Because more than half of the training name pairs are from Latin language clusters, the general transliteration and language models adopted the Latin name transliteration patterns.

it obtained reasonable performance (20-30% CERs) on Latin language names, such as Spanish, English and French names, but strikingly high (over 70%) CERs on oriental language names such as Chinese and Japanese names, even though the Chinese cluster has the most training data.


In a nutshell, we suggest applying the noisy dictionary of predicted names over the test corpus, and use the approximate (predicted) name to non-name proportions over the test set itself to filter out ambiguous names.

our approach does not require large amount of annotated training data.


Similarly with the CUHTK (WER 35.6%) and NIST-B1 (WER 33.8%) and Sheffield (WER 24.6 %) systems we obtained improvements of 1.6%, 0.39% and 0.05% respectively using the Simple Aligned method.

with increasing WER, the named entity word error rate increases significantly, and therefore the benefits of our method are more apparent in such situations.


This link between adjacent tags through an observed-child node allows for a probabilistic relationship to exist between the adjacent tags.

future words can influence tags, which is not true for the CMM.


In finding the easiest word, we use the appropriate local classifier according to the availability of the neighboring tags.

in the first iteration, we always use the local classifiers trained with no contextual tag information (i.e.


Many key phrases are person and location names, which are phonetically translated and whose written forms resemble their pronunciations.

it is possible to discover these translation pairs through their surface strings.


After incorporating the frequencydistance model, correct translations have the maximum frequency-distance weights and are more likely to be selected as the top hypothesis.

the combined model obtained the highest translation accuracy.


We also compare our system with LiveTrans, which only searched within English web pages, thus with limited search space and more noises (incorrect English candidates).

it was more difficult to select the correct translation.


This is due mainly because proper names, which represent most of the Named Entity items, are a very dynamic category of words, strongly related to the period of time representing the documents to process.

this mismatch is inevitable when dealing with archives spreading over a long period of time and containing multiple domain information.


Indeed while the key point of NER systems on written text is their generalization capabilities when processing unknown words, this feature is not relevant for ASR transcripts as the system cannot generate words out of the lexicon (there are no unknown words).

we propose here to fully exploit this constraint (close vocabulary): since the OOV words cannot appear in the ASR transcripts, the NER models can by over-trained on the words belonging to the ASR lexicon.


If the second observation clearly highlights the correlation between the NE distribution in both corpora, it also points out that only 25% of the entities of the unmatched corpus occur in the newsletters corresponding to the same days.

the potential improvement in the overall NER performance is clearly limited.


Also, more important, the techniques rely on different source of data: PRF is a "self-mining" approach while CSSE builds and mines external corpus.


Lexicalized models have a huge number of parameters.

there is a large number of locally optimal parameter settings to which the unsupervised training can be attracted.


Recall that these distinctions are based on absolute performance and not gain.

the best tuned SVM outperforms all other models on this task and metric.


While we are not surprised that the positive gains are small, 7 we are surprised at how often ensemble selection hurts performance.

we investigated some of the metrics where ensemble selection hurts performance and found that ensemble selection overfits the tuning data.


For each one of the 44 words, PowerWord 2002 provides more Chinese translations than the number of its synsets in WordNet 1.7.

the annotator simply selects the Chinese translations that he considers a best match to the corresponding English senses.


To decide the optimal length of text snippets to retrieve, we carried out pilot experiments with two length settings: 250 (≈ 110 English words) and 400 (≈ 175 English words) Chinese characters, and found that more context words helped improve WSD performance (results not shown).

we retrieve text snippets with a length of 400 characters.


For example, translations of senses of church and hearth appear only infrequently in Chinese text.

it is hard to build sense examples for these words.


To make these rich syntactic annotations accessible for linguists, the development of powerful query tools is an obvious need and has become an important task in computational linguistics.

a number of treebank query tools have been developed.


One of the design decisions for the development of the treebank was the commitment to reusability.

the choice of the syntactic annotation scheme should not reflect a particular syntactic theory but rather be as theoryneutral as possible.


The TIGER corpus and the TüBa-D/S have each about 40.000 trees.

one may argue that this fact alone makes MONA unsuitable for use by linguists.


Search engines are extremely helpful for several linguistic tasks, such as computing usage statistics or finding a subset of web documents to analyze in depth; however, these engines were not designed as building blocks for NLP applications.

the applications are forced to issue literally millions of queries to search engines, which limits the speed, scope, and scalability of the applications.


In this stage, each document that matches a query has to be retrieved from an arbitrary location on a disk.

3 the number of random disk seeks scales linearly with the number of documents retrieved.


The goal of an IE system is to discern which of the labels it extracts are in fact elements of C, based on repeated draws from the urn.

the central question we are investigating is: given that a particular label x was extracted k times in a set of n draws from the urn, what is the probability that x ∈ C?


Unlike URNS, our frequency-based assessment does not yield accurate probabilities to associate with each extraction, but for the purpose of returning a ranked list of high-quality extractions it is comparable to URNS (see Figures 3 through 6), and it has the advantage of being much faster.

in the experiments reported on below, we use frequency-based assessment as part of KNOWIT-NOW.


This insight suggests that acoustic models need to be cognizant of potential pronunciation changes.

the lexical and acoustic models should work hand in hand.


It follows from this phonetic detection based on the most reliable features may handle highly variable speech more robustly than systems which demand full identity over all the features for a given phone or phone sequence.

a number of researchers have previously suggested using certain broad classes of segments, rather than full phonemic identification, for a first pass on recognition.


Often there is enough residual phonetic evidence of the deleted phone, or enough phonetic evidence in other parts of the word, to recognize a word correctly despite the deletion.

we decided to use a two-part strategy in calculating canonical pronunciation (Table 2).


As Experiment 2 shows, many words have pronunciations that do not appear in the dictionary.

we must find a way to alleviate the mismatch between the phonetic transcription and the dictionary in a way that is plausible for ASR processing.


In speech, speakers effectively express emotions by modifying prosody, including pitch, intensity, and durational cues in the speech signal.

in order to make text-to-speech synthesis sound as natural and engaging as possible, it is important to convey the emotional stance in the text.


Lastly, emotions blend and transform (Liu, Lieberman and Selker, 2003).

emotion and background mood of immediately adjacent sentences, i.e.


As noted in section 4.3, only 9.87% of the sentences were annotated with a positive emotion, and the results for this class are worse.

performance seems likely to improve as more annotated story data becomes available; at this point, we are experimenting with merely around 12% of the total texts targeted by the data annotation project.


Moreover, arousal appears to be an important dimension for emotional prosody (Scherer, 2003), especially in storytelling (Alm and Sproat, 2005).

we are planning on exploring degrees of emotional intensity in a learning scenario, i.e.


(Schamber and Bateman, 1996), may exist and influence a user's preference for a document.

it is not surprising that some of the causal relationships, such as the link between readable and readability score, are missed in the final graph because of the limitation of the learning algorithms.


Implicatures in questions either waste computational effort or impair the performance of a QA system or both.

when answering questions, it is prudent to identify the questions with implicatures before processing starts.


However, N-gram features not closely related to the topic will bring more noise into the system.

we only take the N-grams within a fixed-length window around the topic keywords for features calculation, and pass them as evidence to cascading classifier.


Our program scanned the two corpora of bios and found only 2 out 49320 bios explicitly stating that the subject never attended any school.

for some types of information, it will be much harder to identify null values through evidence from text.


Given the hidden representation of a derivation, a log-linear distribution over possible next moves is computed.

the set D of structurally local nodes and the set f of predefined features determine the inductive bias of an SSN system.


5 This augmented model has a total of 188 non-terminals to represent labels of constituents, instead of the 33 of the original SSN parser.

of lowering the five function labels, 83 new part-of-speech tags were introduced to partition the original tagset of the Treebank.


If they fail, they increase the domain size and try again, until either they find a model or their resources run out.

although there are infinitely many models satisfying fol(drs(T)), model builders generally build a model with a minimal domain size.


In contrast, it has good precision (0.761) but low recall (0.236) for the FALSE class.

there is a correspondence between low word overlap and FALSE examples (see Example 731 in the Introduction, where important words in the hypothesis like "swamps" or "marshes" are not matched in the text); high overlap, however, is normally necessary but not sufficient for TRUE entailment (see also Example 78 in Section 3).


Due to the low recall, the overall accuracy of the system (0.52, see Table 1) is not significantly higher than the baseline.

this feature behaves in the opposite way to shallow lexical overlap and overgenerates the FALSE class.


As discussed in Section 3.5 we now try to compensate for missing knowledge and improve recall for TRUE entailments by approximating entailment with the features that are furnished by the model builder.

experiment 4 uses all eight deep semantic analysis features, including the features capturing differences in domain-and modelsizes.


The currently available POS taggers for German do not capture more complex morphological features like number or case.

in order to lemmatize German nouns it is necessary to first categorize them into the classes defined above.


However, a noun that could not be lemmatized within one text may well have enough context information for a precise lemmatization within another.

our main idea here is to create a self-learning lexicon that evolves with the nouns processed by the algorithm, continuously learning the correct values for each lexical entry.


The best performing systems at the CoNLL-2005 shared task included a combination of different base subsystems to increase robustness and to gain coverage and independence from parse errors.

they are closely related to the work of this paper.


Sentence in sub-figure (3) is slightly more complicated: the governor of "herself" is "description," and the accessible subject is "Miss Smith."


Consider the toy dataset in Table 2 consisting of four supervised instances with a single explanatory variable.

the logistic regression model has two parameters and takes the following form: A surface plot of L is shown in Figure 2.


This includes all dynamic situations (punctual or durative) that happen or occur in the text, but also states in which something obtains or holds true, if they are temporally located in the text.

generics and most state-denoting expressions are filtered out (see Saurí et al.


A TimeML mark-up of these cases will tag only the complement as an event, disregarding the copular verb.

the modality, tense, aspect, and non-finite morphology information associated with the verb is incorporated as part of the event identified as the nominal or adjectival complement.


Finally, identifying events from adjectives takes a conservative approach of tagging as events only those adjectives that were annotated as such in Time-Bank1.2, whenever they appear as the head of a predicative complement.

in addition to the use of corpus-based data, the subtask relies again on a minimal contextual parsing capable of identifying the complements of copular predicates.


In event-denoting expressions that contain a verbal chunk, tense, aspect, and non-finite morphology values are directly derivable from the morphology of this constituent, which in English is quite straightforward.

the identification of these features is done by first extracting the verbal constituents from the verbal chunk (disregarding adverbials, punctuation marks, etc.


In addition, it is conceived from a highly modular perspective.

an effort has been put on separating linguistic knowledge from the processing thread.


One of the key tasks in natural language processing is that of Information Extraction (IE), which is traditionally divided into three subproblems: coreference resolution, named entity recognition, and relation extraction.

iE corpora are typically annotated with information corresponding to these subtasks (MUC (Grishman, 1995), ACE (NiST, 2000)), facilitating the development of systems that target only one or a subset of the three problems.


However, because 'vans' is not an ACE markable, it cannot participate in an annotated relationship.

'troops' is annotated as being LOCATED in 'city', which makes sense due to the transitivity of the relation LOCATED.


If the two paths have different lengths, they correspond to different ways of expressing a relationship -for instance, they may pass through a different number of predicate argument structures.

the kernel is defined to be 0 in this case.


Many learning tasks have subtasks for which much training data exists.

we want to transfer learning from the old, generalpurpose subtask to a more specific new task, for which there is often less data.


We index the CRFs by i, so that the state of CRF i at time t is denoted s i t .

the feature functions for CRF i are of the form x, t)-that is, they depend not only on the observed input x and the transition ( wt appears in list of first names, last names, honorifics, etc.


Unlike the CoNLL entity recognition data set, in which only proper names of entities are annotated, the ACE data includes annotation both for named entities like United States, and also nominal mentions of entities like the nation.

although the input text has similar distribution in the CoNLL NER and ACE data set, the label distributions are very different.


To derive a version of this formula that is a continuous function of λ, we will need multiple translations t s,1 , ..., t s,K for each source sentence s. The general idea is to weight each of these translations by a factor w(λ, s, k), proportional to the score m λ (t s,k |s) that t s,k is assigned by the log-linear model for a given λ: where α is the smoothing factor.

in the smoothed version of the NIST function, the term I n (t s , r s ) in equation 3is replaced by k w(λ, s, k)I n (t s,k , r s ), and the term C n (t s ) is replaced by k w(λ, s, k)C n (t s,k ).


It must be noted that, because of the final histogram filtering we apply on libraries (retain only the 20 most frequent translations of any source phrase), library B 1 -g1 is not a strict subset of B 1 -g2.

filtering on gap-size usually represents a tradeoff between more frequent long-spanning bi-phrases and less frequent short-spanning ones.


In equation 7, the language model only determines the probability of the words within the target part of the phrase, and not across the phrase boundaries, because we consider only the single target phrases without context.

we assumed that the language model would not have much influence on the confidence estimation and also investigated a model without a language model.


We modified this confidence measure because we found that the average lexicon probability used there is dominated by the maximum.

we determine the maximal translation probability of the target word e over the source sentence words: where f 0 is the "empty" source word (Brown et al., 1993).


Investigations on the use of the IBM-1 model for word confidence measures showed promising results (Blatz et al., 2003;Blatz et al., 2004).

we apply this method here in order to compare it to the other types of confidence measures.


Our evaluation metric only rewards the algorithm for selecting the target word/phrase that happened to be used in the actual translation.

accuracies measured using this metric may be artificially low.


The word translation task and the word-sense disambiguation task have the same form: each word a is associated with a set of possible labels U a ; given a sentence s containing word a, we must determine which of the possible labels in U a to assign to a in the context s. The only difference in the two tasks is the set U a : for word translation it is the set of possible translations of a, while for word sense disambiguation it is the set of possible senses of a in some ontology.

we may use any word sense disambiguation algorithm as a word translation algorithm by appropriately defining the senses (assuming that the WSD algorithm does not assume that a particular ontology is used to choose the senses).


In our setting, however, training data is acquired automatically from bilingual corpora, which are widely available and quite large.

discriminative training is a viable option for the word translation problem.


For example, Carpuat and Wu (2005) obtain negative results for two methods of incorporating the output of a word-sense disambiguation system into a machine translation system.

we instead used our word translation model for a simplified translation problem.


The final result was a set of 655 sentences with a total of 3018 blanks.

for example, the following English-french sentence pair contains the two ambiguous words address and issue and one possible translation for each, examiner and question: • the commission should address the issue once and for all.


An advantage of this task is that, for a given distribution P (t|s), we can easily write a decoder which exhaustively searches the entire solution space for the best answer (provided that there are not too many blanks and that P (t|s) is sufficiently "local" with respect to t).

we can be sure that it is the probability model, and not the decoder, which is determining the quality of the output.


As we have mentioned, this model does not take full advantage of the context in the English sentence.

we hope that incorporating the word translation model into the decoder will improve performance on this task.


This sensitivity to headwords is achieved by propagating lexical heads and POS tags to the nonterminals in the parse tree.

any statistic based on headwords may also be sensitive to the associated POS tag.


Of the three estimatesP 2,1 ,P 2,2 , andP 2,3 , onlyP 2,1 retains the information that the noun is plural and the verb is singular.

p 2 will be sensitive to the morphological clash between gatos and corrió only if λ 2,1 is high, reflecting a high level of confidence in the estimate ofp 2,3 .


The morphological features are local, being restricted to dependencies between words in the parse tree; the reranking features are more global, relying on larger portions of parse structures.

we see our final model as combining the strengths of two complementary approaches.


Two strings γ, γ ∈ V * I are synchronous if each index in index(γ) occurs only once in γ, each index in index(γ ) occurs only once in γ , and index(γ) = index(γ ).

synchronous strings have the general form T are finite, disjoint sets of nonterminal and terminal symbols, respectively, S ∈ V N is the start symbol and P is a finite set of synchronous productions, each of the form Based on an example from (Yamada and Knight, 2001), we provide a sample SCFG fragment translating from English to Japanese, specified by means of the following synchronous productions: Note that in production s 2 above, the nonterminals VB and TO generated from nonterminal VB2 in the English component are inverted in the Japanese component, where some additional lexical material is also added.


Then, in each of the q blocks in the codomain of π r , the h-th element is pebbled while the k-th is not.

the h-th elements of any two consecutive blocks in the codomain of π r must belong to two distinct segments, since at least one intermediate element is not pebbled.


The only difference is that, in LTAG-spinal, syntactic dependencies are represented via direct or local connections.

to better understand the meaning of this difference, we relate it to Frank's (2002)  in a sense, the LtAG-spinal grammar opens a door to a parallel mechanism of building the elementary trees and the derivation tree.


Arguments in Propbank are not obligatory complements.

we cannot treat the Propbank arguments as the arguments in LTAG.


As shown in (Shen and Joshi, 2005), various irrecoverable tree transformations were used to extract the LTAG-spinal treebank according the Propbank annotation on PTB.

we use syntactic dependency for evaluation.


For the synonym, antonym, hypernym, and hyponym questions, the correct answer is the highest frequency word of all the words chosen from WordNet that satisfy all the criteria.

the distractors are of the same POS and similar frequency to the synonym, antonym, or whatever word is the correct answer, as opposed to the target word.


The correlations between the accuracy on each of the nine question types and the Nelson-Denny vocabulary subtest were fairly high (.61 < r < .85, p=.01 for all comparisons).

both the computer and humangenerated questions show good correspondence with an external assessment of vocabulary skill.


According to Pickering and Garrod (2004), the act of engaging in a dialog facilitates the use of similar representations at all linguistic levels, and these representations are shared between speech production and comprehension processes.

structural adaptation should be observed in a dialog setting, both within and between speakers.


As can be seen in Tables 2 and 3, a lower frequency induces a more limited paradigmatic modifiability for a particular sel (which is, of course, expressed as a higher probability value; see the column labeled mod sel in both tables).

with s being the number of distinct possible selections for a particular k, the k-modifiability, mod k , of an n-gram can be defined as follows (f stands for frequency): The paradigmatic modifiability, P -M od, of an ngram is the product of all its k-modifiabilities: Comparing the trigram P -M od values for k = 1, 2 in Tables 2 and 3, it can be seen that the term "long terminal repeat" gets a much higher weight than the non-term "t cell response", although their mere frequency values suggest the opposite.


10 By contrast, we choose a large and already consensual terminology to identify the true terms in our candidate sets.

we are able to dynamically examine various m-highest ranked samples, which, in turn, allows for the plotting of standard precision and recall graphs for the entire candidate set.


That is, we wish to find a small set of sentences that would lead to the greatest reduction of currently unknown words Finding these sentences is a NP-hard problem because the 0/1 knapsack problem could be reduced to this problem in polynomial-time (Gurari, 1989).

we developed an approximation algorithm for finding sentences with the maximum word M : number of tokens will be annotated.


We see that the taggers trained on data selected by MWC outperform those trained on randomly selected data.

in the main experiments, we always use MWC to select the set of manually tagged data for training T anno .


In contrast to them, we resort to parallel corpora as a source of semantic equivalence.

we avoid the need for a target concept dictionary in addition to the English FrameNet.


Note that in this case the lexicon is used only to improve chunking, not for correction.

coverage of the lexicon is far less important.


The errors of the chunking and correction models coincided as they both rely on the same language model.

chunking errors are unlikely to reduce the correction performance.


Note that for handling word merge/split errors we need to learn the character distributions within lines, rather than within words as we normally do.

more training data is required for reliable parameter estimation.


Luckily, most of these less-represented languages belong to language families with several prominent members.

some of these languages have siblings with more resources and published research.


The entropy term does not involve any of the pairwise distributions.

ifP is fixed, to maximize d we need to maximize each of the bilingual crossentropy terms.


domain-specific and technical terms) can often be translated simply by inferring morphological rules transforming words of one language into another.

one may expand the dictionary coverage using non-parallel texts in both languages, or even in just one language if its morphology is sufficiently regular.


In general, this matching procedure assumes that frames describing a syntactic argument structure similar to that of the parsed sentence are more likely to correctly describe the semantic roles of the extracted arguments.

the frame matcher only chooses roles from frames that are the best syntactic matches with the extracted argument set.


The specialization procedure consists of first identifying features f k ij to which correspond more than one semantic relation, then replace these features with their hyponyms synsets.

one feature breaks into several new specialized features.


In general, the most common approaches start by evaluating the co-occurrence matrix of features versus contexts of instances of ambiguous word, given sense-tagged training data for this target word.

contexts are usually represented in a highdimensional sparse feature space, which is far from optimal for many classification algorithms.


While LexRank outperforms the baseline system on the first two clusters both in terms of MRR and TRDR, their performances are not substantially different on the third cluster.

we examined properties of the questions within each cluster in order to see what effect they might have on system performance.


Much previous research on automatic extraction of opinion information performed classifications at the sentence level.

we define sentence-level opinion classifications in terms of the phrase-level annotations.


With the exception of question venezuela 3, which does not contain a clear source (and is problematic altogether because there is only a single answer in the corpus and the question's qualification as opinion is not clear) the source filter always ranked an answer within the first 25 answers.

source filters can be especially useful in systems that rely on the presence of an answer within the first few ranked answer segments and then invoke more sophisticated analysis in the additional processing phase.


Early on in a pilot study, researchers discovered that it was impossible for assessors to consistently enumerate the total set of nuggets contained Let r # of vital nuggets returned in a response a # of okay nuggets returned in a response R # of vital nuggets in the answer key l # of non-whitespace characters in the entire answer string in a system response, given that they were usually extracted text fragments from documents (Voorhees, 2003).

a penalty for verbosity serves as a surrogate for precision.


In comparative evaluations, we ultimately want to determine if one technique is "better" than another.

the system rankings produced by a particular scoring method are often more important than the actual scores themselves.


As illustrated in Figure 1, over 80% of queries include more than one search term.

word ngram probabilities provide useful statistical knowledge that can be exploited to improve spelling correction.


Selected terms are added to the initial query.

an expanded query consists of the initial terms and added terms.


In the above procedures, we naturally introduced transductive learning into query expansion as the effective way in order to automatically find some relevant documents.

we do not need to modify a basic query expansion procedure and can fully utilize the potential power of the basic query expansion.


They may choose another relevant and non-relevant documents which rank in top 10.

we test an another experiment where relevant and non-relevant documents are selected randomly from top 10 rank.


The essential idea behind the lexical cohesion approaches is that different topics will have different vocabularies.

the lexical cohesion within topics will be higher than the lexical cohesion between topics, and gaps in cohesion may mark topic boundaries.


Using a vector space method without singular value decomposition, Hearst (1997) reports an F-measure of .70 when detecting topic shifts between paragraphs.

previous work suggests that the Hearst (1997) method is superior to that of Foltz et al.


Since each vector in the basis is orthogonal, the basis represents all linear combinations of what has been previously said.

the component of a new utterance vector that is parallel to the basis is already represented by a linear combination of the existing vectors.


Additionally, tutors were instructed to introduce problems using a fixed set of scripted problem statements.

each topic shift corresponds to a distinct problem introduced by the tutor.


These bridging inferences are a kind of collocational cohesion (Halliday and Hassan, 1976) whereby words that cooccur in similar contexts become highly related in the LSA space.

in applications where this kind of inferencing is required, one might expect an LSA based method to excel.


Different topics, however, are characterized by distinctive terminology and typical word co-locations.

given a pre-divided corpus, similar co-occurrence patterns would typically be extracted from the same topical sub-corpus.


different religions), the tendency to form topic-specific clusters intensifies regardless of factors that otherwise could have impact this tendency, such as the co-occurrence window size.

corresponding key terms of different topics may not be assigned by a standard method to the same cluster, in contrast to our goal.


One possible reason for this is that during the course of learning, the model will see many segments that are nearly correct, e.g., segments that overlap correct segments and differ by a single token.

the model learns to score all segments containing even a small amount of negative evidence as invalid in order to ensure that these nearly correct segments have a sufficiently low score.


Multiple pattern recognition tasks may share the same acoustic features: for example, energy and NCCCs are used for pitch tracking, and energy and MFCCs can be used in vowel classification and discrete sound recognition.

it is more efficient to decouple feature extraction from pattern recognition, as is shown in Figure 3.


In this way, it is possible to train phone models using a training set that covers all possible phones, and then construct an application-specific discrete command vocabulary without retraining by recombining existing phone HMMs into new word HMMs.

each VJ-driven application can have its own appropriate discrete sound set.


In spoken dialogue interfaces, users often make utterances before the query is completely generated in their mind; thus input queries are often vague or fragmental.


However, this also means that users can start utterances before queries are thoroughly formed in their mind.

input queries are often vague or fragmental, and sentences may be ill-formed or ungrammatical.


In such cases, an enormous list of possible relevant documents is usually obtained because there is very limited information that can be used as clues for retrieval.

it is necessary to narrow down the documents by clarifying the user's intention through a dialogue.


Therefore, we define IG H(S) for a candidate question S by the following equations.

here, D k denotes the k-th retrieved document by matching the query to the KB, and CM (D) denotes the matching score of document D. C i represents the number of documents classified into category i by candidate question S, which is weighted with the matching score.


Calculate entropy H(m) for every word based on probability P (i).

this P (i) is calculated with the occurrence count N (m) of word m that appears in the text corpus and the count  we selected 40 words that have a large value of entropy.


The subjects were allowed to utter a query again up to twice per task if they thought an adequate retrieval result was not obtained.

we collected 238 utterances for 196 (=14 × 14) tasks in total.


Specifically, the users might not have answered the questions appropriately, or the replies might not have been correctly recognized.

we also evaluated with the following condition.


A dialog policy can be thought of as an enumeration of all of the states a dialog system can be in, and the corresponding action to take from each of those states.

a policy completely specifies the behavior of a dialog manager.


The problem with testing our policies against actual users is that there are a number of aspects of dialog that we have not modeled, such as nonunderstandings, misunderstandings, and even parsing sentences into the action specification and generating sentences from the action specification.

as a simplification we tested our learned system policy on the handcrafted baseline policies.


We see that although the performance of the learned policy is not as good as with the handcrafted pair, the performance is close, with the major shortcoming being a general increase in dialog length.

the policies that we have learned our robust against different strategies a user might want to use.


This approach of learning the user policy allows us to minimize human involvement, as neither a training corpus must be collected nor a simulated user built.

the only human input required for this approach was to define the domain task and to define success in that domain.


Moreover, they may be used discriminatively to label novel verb instances for semantic role.

we evaluate them both in terms of the verb alternations that they learn and their accuracy as semantic role labelers.


Typically, these measures rely on an extensive hierarchy of hyponymy relationships for nouns.

these measures are expected to perform poorly when used to estimate distance between senses of part-of-speech pairs other than noun-noun, not just because the WordNet hierarchies for other parts of speech are less well developed, but also because the hierarchies for the different parts of speech are not well connected.


The set of Rubenstein and Goodenough word pairs is much too small to safely assume that measures that work well on them do so for the entire English vocabulary.

semantic measures have traditionally been evaluated through applications that use them, such as the work by Hirst and Budanitsky (2005) on correcting real-word spelling errors (or malapropisms).


The aim of SMR consists in using an SMT system to deal with reordering problems.

the SMR system can be seen as an SMT system which translates from an original source language (S) to a reordered source language (S'), given a target language (T).


• The new task S'2T becomes more monotonous.

the translation units tend to be shorter and SMT systems perform better.


A tutoring SDS (Litman and Silliman, 2004;Pon-Barry et al., 2004) has to discuss concepts, laws and relationships and to engage in complex subdialogues to correct student misconceptions.

dialogues with such systems have a rich discourse structure.


We hypothesize that an incorrect answer to the first tutor question is indicative of the system's picking of a topic that is problematic for the student.

we see more learning in students for which more knowledge gaps are discovered and addressed by ITSPOKE.


We are also interested in the contribution of each single feature.

we ran the classifier again, leaving out one feature at a time.


Postolache and colleagues also observe a slowly flattening learning curve after a very small amount of data (even 1%, in their case).

they predict an increase in performance will mainly come from better features rather than more training data.


In the case of Macro-F, each category is treated as one unit, independent of the number of items contained in it.

the classification success of the individual items in rare categories is given more importance than classification success of frequent category items.


This right sentence bracket is positioned between the Mittelfeld and the Nachfeld.

the theory of topological fields states the fundamental regularities of German word order.


The crossing branches in the Negra treebank cannot be processed by most probabilistic parsing models since such parsers all presuppose a strictly context-free tree structure.

the Negra trees must be transformed into proper trees prior to training such parsers.


Crossing branches do not only arise with respect to the subject at the sentence level but also in cases of extraposition and fronting of partial constituents.

approximately 30% of all Negra trees contain at least one crossing branch.


As a result, approximately 30% of all Negra trees contain at least one crossing branch.

tree transformations have a major impact on the type of constituent structures that are used for training probabilistic parsing models.


Due to the word order freeness of phrases, a given NP in any one position may in principle fulfill different grammatical functions in the sentence as a whole.

grammatical functions need to be explicitly marked in the treebank and correctly assigned during parsing.


Training is complete after multiple passes through the whole corpus.

we decode using the Chu-Liu-Edmonds (CLE) algorithm due to its speed advantage over ILP (see Section 5.2 for a detailed comparison of runtimes).


Also, only a small number of sentences take longer than two minutes to parse.

in practice we would not see a significant degradation in performance if we were to fall back on the CLE algorithm after two minutes of solving.


While this is not crucial during decoding, it does make discriminative online training difficult as training requires several iterations of parsing the whole corpus.

table 3: Labelled accuracy (LAC) and total solve time (St) for the cross dataset using varying q values and the Chu-Liu-Edmonds algorithm (CLE) we investigate if it is possible to speed up our inference using a simple approximation.


Processing such questions requires not only knowing what the important people, places and other entities are, but also what kind of events they are involved in, the roles they play in those events, and the relations among those events.

we suggest that identifying such events in a text should play an important role in systems that attempt to address questions like these.


Each row in Table 6 corresponds to a model trained on the feature sets named in that row and all the rows below it.

on the top row, no feature sets have been removed, and on the bottom row only one feature set remains.


This definition is application independent and enables annotating test datasets that evaluate directly lexical matching models.

we created a dataset annotated for lexical reference, using a sample of sentence pairs (texthypothesis) from the 1st Recognising Textual Entailment dataset.


However, for proper entailment it is further needed that the right relations would hold between these concepts 2 .

lexical entailment should typically be a component within a more complex entailment modeling (or semantic matching) system.


As Gildea & Jurasksy (2002) state, "the less-specific distributions were used only when no data were present for any more-specific distribution.

the distributions selected are arranged in a cut across the lattice representing the most-specific distributions for which data are available."


The execution time for PML is about 8 times longer than kNN for 1.7k training sentences, but PML ultimately runs faster than kNN on all 39.8K training sentences (and, extrapolating from the graph in Figure 6, on any larger datasets).

pML seems generally more suitable for large training data.


Understanding the temporal flow of discourse is a significant aspect of text comprehension.

temporal analysis has been a focus of linguistic research for quite some time.


For instance, a medical case summary first discusses various developments in the medical history of a patient and then focuses on his current conditions.

the first part of the summary contains many short temporal segments.


Also, it has been showed that active learning can be effectively applied to the SVMs (Schohn and Cohn, 2000;Tong and Koller, 2000).

we choose the SVMs as the topic classifier.


Meanwhile unlabeled sentences are relatively easy to collect.

to reduce the cost of labeling training utterances, we employ weakly supervised techniques for training the topic and semantic classifiers.


When we use the most relevant data chunk (about 20 million words) to re-rank the N -best list, 36 chunks of data will be used at least once for 919 different sentences, which accounts for about 720 million words in total.

the x-axis in figure 3 should not be interpreted as the total amount of data used but the number of the most relevant corpora used for each sentence.


The contribution of the columns k through N to the new heuristic H M1 (l, n, i, k) is guaranteed to be greater in cost than their contribution to the old heuristic.

meaning that the heuristic is monotonic or consistent.


When the outside estimate is admissible and monotonic, the exploration is optimal in the sense that whenever a hypothesis is taken from the top of the agenda, it is a Viterbi translation of the corresponding target substring.

when S[0, N, s , /s ] is added to the chart, we have found the Viterbi translation for the entire sentence.


However, because our purpose is to generate new words, the use of statistics obtained from ex-isting corpora is not effective.

we consider P (K) to be constant for every K. In summary, P (K|R, W ) is approximated by a product of P (R|K) and P (W |K).


However, unlike Equation (2), the numbers of w i and k j derived from W and K are not always the same, because users are allowed to provide an arbitrary number of impression keywords.

for each k j we select the w i that maximizes P (w i |k j ) and approximate P (W |K) as shown in Equation (4).


However, it is desirable to collect an association between a word and a character, not simply their co-occurrence in corpora.

we used a dictionary of Kanji in Chinese, in which each Kanji character entry is explained via sentences, and often exemplified by one or more words that include that character.


Because Chinese sentences lack lexical segmentation, we used SuperMorpho 1 to perform a morphological analysis of explanation sentences and example words.


We merged these candidates, removed duplications, and sorted the remaining candidates by the character code.

the assessors judged the correctness of up to 200 candidates for each test word.


Because our method uses the impression model to re-rank the candidates produced by the pronunciation model, the lists for the control and our method comprise the same candidates.

it is fair to compare these two methods by the average rank of the correct answers.


For example, Chinese does not allow consonant clusters or coda consonants except [n, N], and this results in deletion, substitution of consonants or insertion of vowels.

while a syllable initial /d/ may surface as in Baghdad " !


In general, we may expect topically related names in different languages to tend to co-occur together over time.

if we have comparable news articles over a sufficiently long time period, it is possible to exploit such correlations to learn the associations of names in different languages.


Retrieval precision will be negatively affected by incorrect classification of native and foreign words.

we consider that keeping the proportion of false positives -correct Arabic words identified as foreign (precision) -low to be more important than correctly identifying a higher number of foreign words (recall).


We view summarization as extraction of important sentences from the text.

of the merging process the layout of the documents is lost.


This indicates that paragraph boundary identification requires features usually used for style analysis and ones describing cohesive relations.

paragraph boundary identification is in fact a task which crosses the borders between content and style.


We chose not to include pronouns in the BEST-CUT initial graphs, because, since most features are oriented towards Named Entities and common nouns, the learning algorithm (maxent) links pronouns with very high probability to many possible antecedents, of which not all are in the same chain.

in the clusterization phase the pronouns would act as a bridge between different entities that should not be linked.


These results mean that the recall of an IE system for interacting proteins is improved for a large amount of text even if it is low for a small corpus.

this justifies our assertion that a high degree of precision in the low-recall range is important.


From Figures 6 and 7, the training corpus (207 abstracts at a maximum)  2is not large enough.

increasing corpus size will further improve IE accuracy.


As first shown by Anfinsen (1973), protein folding is a reversible process: under "denaturing" conditions, proteins typically unfold into a random state (which still preserves the chain connectivity of the primary amino acid sequence), and refold again into their unique native state if the natural folding conditions are restored.

all the information that is necessary to determine the folded structure has to be encoded in the primary sequence.


Unlike in standard CKY, each cell contains the full set of structures for its substring (which leads to the exponential worst-case behavior observed above).

the chart does not need to be unpacked to obtain the desired output structure.


And any given sentence has a large number of possible interpretations, just as any amino acid sequence has an astronomical number of possible spatial conformations.

it is not surprising if similar techniques can be applied to both tasks.


Rather than imagining the parser roaming around the tree for picking up the infor-mation it needs, we rather relabel the nodes to directly encode this information.

rather than have the parser "look" to find out that, say, the parent of some N P is an S, we simply relabel the N P as an N P [S].


This is exactly what is done by Klein and Manning (2003).

the "phrasal categories" of our title refer to these new, hybrid categories, such as N P [S].


Moreover, the syntactic types that determine the applicability of rules in derivations are transparent to (i.e., are determined, though not necessarily uniquely, by) the semantic types that they are associated with.

syntactic types are more expressive and more numerous than standard parts of speech: there are around 500 highly frequent CCG types, against the standard 50 or so Penn Treebank POS tags.


But structural priming predicts that a target category will occur more frequently closer to a potential prime of the same category.

we can correlate the probability of repetition with the distance between prime and target.


As a step up from INHERIT, we propose an INHERIT2 model where each nonterminal carries two features.

we will have L 6 R 3 binary rules instead of L 3 R 3 .


When modeling the lexical rule P (X[α] → w), we could use features that consider the spelling of the word w in conjunction with the value of α.

we might learn that V [1] is particularly likely to rewrite as a word ending in -s. Spelling features that are predictable from string context are important clues to the existence and behavior of the hidden annotations we wish to induce.


We use speech segments as the unit of classification because they represent natural discourse units.

we are able to exploit relationships at the speech-segment level.


In particular, due to high coreference annotation costs, data sets that are annotated with opinion information (like ours) do not typically include supervisory coreference information for all noun phrases in a document (as would be required for the application of traditional coreference resolution techniques), but only for noun phrases that act as opinion sources (or targets).

we define the task of partially supervised clustering, the goal of which is to learn a clustering function from a set of partially specified clustering examples (Section 4).


Motivated by the desire to utilize unlabeled examples (discussed later), we define training as an integrated task in which pairwise NP coreference decisions are learned together with the clustering function as opposed to treating each NP pair as a training example.

our training phase takes as input a set of documents with manually annotated opinion sources together with coreference annotations for the sources; it outputs a classifier that can produce source coreference chains for previously unseen documents contain-ing marked (manually or automatically) opinion sources.


We use the same test set for all experi-3 For the transitive closure, n is the number of items in a document, which is O( √ k), where k is the number of NP pairs.

transitive closure is sublinear in the number of training instances.


A number of studies have investigated sentiment classification at document level, e.g., (Pang et al., 2002;Dave et al., 2003), and at sentence level, e.g., (Hu and Liu, 2004;Nigam and Hurst, 2005); however, the accuracy is still less than desirable.

ranking according to the likelihood of containing sentiment information is expected to serve a crucial function in helping users.


Note that the relevance models are constructed using training data for the training-based task, but are constructed using test data for the seed-based task, as mentioned in Section 4.1.

the base model is only used for the training data, not for the test data, in the training-based task, while it can be performed for the test data in the case of the seed-based task.


4 This data set was annotated with sentiment polarities at the phrase level, but not explicitly annotated at the sentence level.

we provided sentiment polarities at the sentence level to prepare training data and data for evaluation.


Some expressions are sometimes used to express a certain topic, such as settlements in "Israeli settlements in Gaza and West Bank"; but at other times are used to express a certain sentiment, such as the same word in "All parties signed courtmediated compromise settlements".

we will use whole sentences to model topic relevance, while we will use the automatically extracted sentiment expressions to model sentiment relevance, in Sections 5.3 and 5.4.


Errors in sentence delimitation or syntactic parsing also result in false candidate atoms.

to adopt a candidate polar atom for the new lexicon, some threshold values for the frequencies or ratios are required, but they depend on the type of the corpus, the size of the initial lexicon, etc.


No manual tuning process is required, so the algorithm only needs unannotated domain corpora and the initial lexicon.

our learning method can be used not only by the developers of the system, but also by endusers.


The lexicon can be expanded automatically by using unannotated corpora, and tuning of the threshold values is not required.

even end-users can use this approach to improve the sentiment analysis.


Overall, the addition of the style and domain models reduces perplexity and WER by a noticeable 7.1% and 2.1%, respectively, as shown in Table 2  In addition to identifying content words, HMM-LDA also assigns words to a topic based on their distribution across documents.

we can apply HMM-LDA with 100 topics to the Textbook dataset to identify representative words and their associated contexts for each topic.


As shown in Figure 8, of the 100 topic models, 15 of them account for over 90% of the total weight.

lectures tend to show a significant topic skew which topic adaptation approaches can model effectively.


As motivated in section 5.2, allowing for contextdependent interpolation weights based on topic labels may yield significant improvement for both perplexity and WER.

in future work, we would like to study algorithms for automatically learning appropriate context-dependent interpolation weights.


A disadvantage of such an approach is that the tagger works on strings rather than the wordlattices that we expect from an ASR system.

we must extract a set of strings from the lattices prior to tagging.


In general, the addition of rare tags, even if they are correct, creates a harder learning problem for EM.

a critical aspect of resource-poor POS tagging is the acquisition of a high-quality lexicon.


Given two hypothesized labelings of the test set, h 1 and h 2 , the one with the lower PAC-Bayesian bound will achieve a lower expected test risk.

one can use the bound as a principled way of choosing the parameters in the Transductive Clustering algorithm: First, a large number of different clusterings is created; then the one that achieves the lowest PAC-Bayesian bound is chosen.


Parsing such sentences gives clues to the expected part of speech of the word that should replace the misspelled word.

candidates produced by the spell checker can be filtered.


However, using the n-gram method will likely indicate that the word trigram "piece of land" is much more likely than the trigram "peace of land."

the word "piece" is a more likely correction than "peace".


But these resources may miss domain-specific senses, which leads to incomplete sense tagged corpus.

sense taggers trained on the incomplete tagged corpus will misclassify some instances if the senses of these instances are not defined in sense inventories.


8 Initially there are no tagged examples for the m-th class in D L .

we do not need to remove tagged examples for this new class, and then directly train a classifier with D L .


The reason to normalize M (C µ k , C k ) is that M (C µ k , C k ) tends to decrease when increasing the value of k (Lange et al., 2002).

for avoiding the bias that the smaller value of k is to be selected as the model order, we use the cluster validity of a random predictor to normalize new senses of w may be represented by the groups in which there is no instance from X L .


Given an incomplete tagged corpus for a target word, SVM does not have the ability to find the new senses from untagged corpus.

it labels all the instances in the untagged corpus with sense tags from S L .


LPU algorithm can deal with only one-class classification problem.

the labeled data of other classes cannot be used when determining the positive labeled data for current class.


For example, if the ground truth sense number of the word w is k w , and the estimated value isk w , then the absolute value of the difference between these two values is |k w −k w |.


LPU does not have the order identification capability since it always assumes that there is at least one new class in unlabeled data, and does not further differentiate the instances from these new classes.

we do not provide the order identification results of LPU.


But split infinitives are relatively rare, so in the vast majority of cases the infinitive "to" will be adjacent to the verb.

we decided that a bigram (e.g., "to protest") should representationally subsume this extraction pattern because the syntactic flexibility afforded by the pattern is negligible.


Indeed, RF techniques have been applied to CBF, or adaptive filtering, successfully (Allan, 1996;Schapire et al., 1998).

it is likely that RF can also be applied to CF.


we only have to search small part of the search space without affecting the performance very much.


This approximation allows us to use the linear interpolation method to mix the approximated Polya distributions.

our model is similar to two-stage language models (Zhai and Lafferty, 2002) that combine the Dirichlet prior method and the linear interpolation method.


(Tseng et al., 2005;Asahara et al., 2005) While the CRF is quite effective compared with other models designed for CWS, one wonders whether it may be limited by its restrictive independence assumptions on non-adjacent labels: an order-M CRF satisfies the order-M Markov assumption that, globally conditioned on the input sequence, each label is independent of all other labels given the M labels to its left and right.

the model only "sees" word boundaries within a moving window of M + 1 characters, which prohibits it from explicitly modeling the tendency of strings longer than that window to form words, or from modeling the lengths of the words.


KnowItAll is based on the observation that the Web corpus is highly redundant.

its selective, high-precision extraction patterns readily ignore most sentences, and focus on sentences that indicate the presence of relation instances with very high probability.


It is of course impossible to know which extractions are correct, but there exist properties of patterns and pattern matches that increase or decrease the confidence in the extractions that they produce.

instead of a binary classifier, we seek a real-valued confidence function c, mapping the set of extracted instances into the [0, 1] segment.


the items i j−1 j−m ) which hasn't been observed during training, a back-off to the corresponding reduced order chain is done 3 : Note that if the 0-th order chain also hasn't been observed during training, we are effectively backing off to the uniform distribution.

a caveat: the training dataset for an author can be much smaller (and hence have a smaller vocabulary) than the combined training dataset for the generic author, resulting in when a previously unseen chain is encountered there is a dangerous bias towards author a, i.e., Kernel based techniques, such as SVMs, allow the comparison of, and discrimination between, vectorial as well as non-vectorial objects.


The amount of annotated data is always a bottleneck for supervised learning methods in practice.

we evaluate the impact of training data size on the NER performance across domains.


However, the performance stability of domain-specific NER model is poor across different domains.

it is very popular to build a general NER model for the general applications in practice.


The performance of PER and LOC recognition are more stable across domains.

few efforts are needed to adapt the existing high-quality general PER and LOC recognition models in domainspecific applications.


Since ORG and MISC NEs usually contain more domain-specific semantic information, ORG and MISC are more domain-dependent than PER and LOC.

more domain-specific features should be mined for ORG and MISC recognition.


The confidence score of the whole sample sentence is defined as the average of the confidence scores of all the annotations contained in the sentence.

we add those sample sentences with lower confidence scores into the training set.


For the two data sets considered in this paper, the average number of realizations produced by the generator is 85.7 and 102.2 (the maximum numbers are 4176 and 3408, respectively).

there is immediate demand for a principled way of choosing a single output among the generated candidates.


With only one sentence labeled as in our case, the semi-supervised method, based on global distribution, makes use of a large amount of unlabeled data to reveal the underlying manifold structure.

the performance is much better than that of a SVM when more sentences are considered.


A simple word overlapping counting (or weighting) is performed between the Topic Signature and the test example 7 .

the occurrence evaluation measure counts the amount of overlapped words and the weight evaluation measure adds up the weights of the overlapped words.


However, N is a huge number on the web and sometimes it is difficult to know exactly.

we regard the co-occurrence matrix as a contingency table: where W represents a given set of words.


highest nine words for each word, groups of ten words.


it can be used easily as a baseline, as suggested by (Lapata and Keller, 2004).